English (United Kingdom)

https://curated-unify.zendy.io/wp-json/zendy-region/v1/featured_content/oa?rat=en

https://curated-unify.zendy.io/wp-json/zendy-region/v1/highlighted_journal/

Global: Zendy Plus annual

Presents the access of premium content as premium feature

Premium Content

Presents the keyphrase highlighting as premium feature

Keyphrase Highlighting

Presents the summarisation as premium feature

Summarisation

Insights

Presents the pdf analysis as premium feature

PDF Analysis

Presents the zaia usage as premium feature

ZAIA

Global: Zendy Tools annual

Global: Zendy Free Plan

Global: Zendy Tools monthly

Global: Zendy Plus monthly

Background  This study intended to determine important genes related to the prognosis and recurrence of breast cancer.    Methods  Gene expression data of breast cancer patients were downloaded from TCGA database. Breast cancer samples with recurrence and death were defined as poor disease-free survival (DFS) group, while samples without recurrence and survival beyond 5 years were defined as better DFS group. Another gene expression profile dataset (GSE45725) of breast cancer was downloaded as the validation data. Differentially expressed genes (DEGs) were screened between better and poor DFS groups, which were then performed function enrichment analysis. The DEGs that were enriched in the GO function and KEGG signaling pathway were selected for cox regression analysis and Logit regression (LR) model analysis. Finally, correlation analysis between LR model classification and survival prognosis was analyzed.    Results  Based on the breast cancer gene expression profile data in TCGA, 540 DEGs were screened between better DFS and poor DFS groups, including 177 downregulated and 363 upregulated DEGs. A total of 283 DEGs were involved in all GO functions and KEGG signaling pathways. Through LR model screening, 10 important feature DEGs were identified and validated, among which,  ABCA3 ,  CCL22 ,  FOXJ1 ,  IL1RN ,  KCNIP3 ,  MAP2K6 , and  MRPL13 , were significantly expressed in both groups in the two data sets.  ABCA3 ,  CCL22 ,  FOXJ1 ,  IL1RN , and  MAP2K6  were good prognostic factors, while  KCNIP3  and  MRPL13  were poor prognostic factors.    Conclusion   ABCA3 ,  CCL22 ,  FOXJ1 ,  IL1RN , and  MAP2K6  may serve as good prognostic factors, while  KCNIP3  and  MRPL13  may be poor prognostic factors for the prognosis of breast cancer.

Prognostic biomarkers related to breast cancer recurrence identified based on Logit model analysis